使用pytorch神经网络时报错TypeError: __init__() takes 1 positional argument but 3 were given

项目场景：

试图加入一个新的loss函数

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问题描述：

在加入新的loss进行计算的时候碰到如标题所示的bug

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原因分析：

原因是没有实例化网络，直接调用类来进行forward

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torchvision.models.vgg import vgg16
import numpy as np

'''Zero-DCE Spatial Consistency Loss'''
class L_spa(nn.Module):

    def __init__(self):
        super(L_spa, self).__init__()
        # print(1)kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
        kernel_left = torch.FloatTensor( [[0,0,0],[-1,1,0],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
        kernel_right = torch.FloatTensor( [[0,0,0],[0,1,-1],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
        kernel_up = torch.FloatTensor( [[0,-1,0],[0,1, 0 ],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
        kernel_down = torch.FloatTensor( [[0,0,0],[0,1, 0],[0,-1,0]]).cuda().unsqueeze(0).unsqueeze(0)
        self.weight_left = nn.Parameter(data=kernel_left, requires_grad=False)
        self.weight_right = nn.Parameter(data=kernel_right, requires_grad=False)
        self.weight_up = nn.Parameter(data=kernel_up, requires_grad=False)
        self.weight_down = nn.Parameter(data=kernel_down, requires_grad=False)
        self.pool = nn.AvgPool2d(4)
    def forward(self, org , enhance ):
        b,c,h,w = org.shape

        org_mean = torch.mean(org,1,keepdim=True)
        enhance_mean = torch.mean(enhance,1,keepdim=True)

        org_pool =  self.pool(org_mean)            
        enhance_pool = self.pool(enhance_mean)    

        weight_diff =torch.max(torch.FloatTensor([1]).cuda() + 10000*torch.min(org_pool - torch.FloatTensor([0.3]).cuda(),torch.FloatTensor([0]).cuda()),torch.FloatTensor([0.5]).cuda())
        E_1 = torch.mul(torch.sign(enhance_pool - torch.FloatTensor([0.5]).cuda()) ,enhance_pool-org_pool)


        D_org_letf = F.conv2d(org_pool , self.weight_left, padding=1)
        D_org_right = F.conv2d(org_pool , self.weight_right, padding=1)
        D_org_up = F.conv2d(org_pool , self.weight_up, padding=1)
        D_org_down = F.conv2d(org_pool , self.weight_down, padding=1)

        D_enhance_letf = F.conv2d(enhance_pool , self.weight_left, padding=1)
        D_enhance_right = F.conv2d(enhance_pool , self.weight_right, padding=1)
        D_enhance_up = F.conv2d(enhance_pool , self.weight_up, padding=1)
        D_enhance_down = F.conv2d(enhance_pool , self.weight_down, padding=1)

        D_left = torch.pow(D_org_letf - D_enhance_letf,2)
        D_right = torch.pow(D_org_right - D_enhance_right,2)
        D_up = torch.pow(D_org_up - D_enhance_up,2)
        D_down = torch.pow(D_org_down - D_enhance_down,2)
        E = (D_left + D_right + D_up +D_down)
        # E = 25*(D_left + D_right + D_up +D_down)
        

        return E

from new_loss import L_spa
if opt.spa_loss:
            spa_loss = torch.mean(L_spa(StyledFirstFrame, FirstFrame))
            Loss = Loss + spa_loss

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解决方案：

加入如下代码进行实例化

if opt.spa_loss:
    L_spa = L_spa()

正常实例化一个简单神经网络就可